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Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study.
Journal of Magnetic Resonance Imaging : JMRI 2018 September 9
BACKGROUND: Accurate classification of gliomas is crucial for prescribing therapy and assessing the prognosis of patients.
PURPOSE: To develop a radiomics nomogram using multiparametric MRI for predicting glioma grading.
STUDY TYPE: Retrospective.
POPULATION: This study involved 85 patients (training cohort: n = 56; validation cohort: n = 29) with pathologically confirmed gliomas.
FIELD STRENGTH/SEQUENCE: 1.5T MR, containing contrast-enhanced T1 -weighted (CET1 WI), axial T2 -weighted (T2 WI), and apparent diffusion coefficient (ADC) sequences.
ASSESSMENT: A region of interest of the tumor was delineated. A total of 652 radiomics features were extracted and were reduced using least absolute shrinkage and selection operator regression.
STATISTICAL TESTING: Radiomic signature, participant's age, and gender were analyzed as potential predictors to perform logistic regression analysis and develop a prediction model of glioma grading, and a radiomics nomogram was used to represent this model. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical value in glioma grading.
RESULTS: The radiomic signature was significantly associated with glioma grade (P < 0.001) in both the training and validation cohorts. The performance of the radiomics nomogram derived from three MRI sequences (with C-index of 0.971 and 0.961 in the training and validation cohorts, respectively) was improved compared to those based on either CET1 WI, T2 WI, or ADC alone in glioma grading (with C-index of 0.914, 0.714, 0.842 in the training cohort, and 0.941, 0.500, 0.730 in the validation cohort). The nomogram derived from three sequences showed good calibration: the calibration curve showed good agreement between the estimated and the actual probability. The decision curve demonstrated that combining three sequences had more favorable clinical predictive value than single sequence imaging.
DATA CONCLUSION: We created and assessed a multiparametric MRI-based radiomics nomogram that may help clinicians classify gliomas more accurately.
LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018.
PURPOSE: To develop a radiomics nomogram using multiparametric MRI for predicting glioma grading.
STUDY TYPE: Retrospective.
POPULATION: This study involved 85 patients (training cohort: n = 56; validation cohort: n = 29) with pathologically confirmed gliomas.
FIELD STRENGTH/SEQUENCE: 1.5T MR, containing contrast-enhanced T1 -weighted (CET1 WI), axial T2 -weighted (T2 WI), and apparent diffusion coefficient (ADC) sequences.
ASSESSMENT: A region of interest of the tumor was delineated. A total of 652 radiomics features were extracted and were reduced using least absolute shrinkage and selection operator regression.
STATISTICAL TESTING: Radiomic signature, participant's age, and gender were analyzed as potential predictors to perform logistic regression analysis and develop a prediction model of glioma grading, and a radiomics nomogram was used to represent this model. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical value in glioma grading.
RESULTS: The radiomic signature was significantly associated with glioma grade (P < 0.001) in both the training and validation cohorts. The performance of the radiomics nomogram derived from three MRI sequences (with C-index of 0.971 and 0.961 in the training and validation cohorts, respectively) was improved compared to those based on either CET1 WI, T2 WI, or ADC alone in glioma grading (with C-index of 0.914, 0.714, 0.842 in the training cohort, and 0.941, 0.500, 0.730 in the validation cohort). The nomogram derived from three sequences showed good calibration: the calibration curve showed good agreement between the estimated and the actual probability. The decision curve demonstrated that combining three sequences had more favorable clinical predictive value than single sequence imaging.
DATA CONCLUSION: We created and assessed a multiparametric MRI-based radiomics nomogram that may help clinicians classify gliomas more accurately.
LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018.
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